P482: ACCUFUSION: A HIGHLY SCALABLE SOFTWARE TOOL FOR DETECTING GENE FUSIONS BY RNA-SEQ IN LEUKEMIA
Authors: Xiaoping Su, Gabriel Malouf
Journal: HemaSphere (2023)

Background: Gene fusions created by somatic genome rearrangements are known to play an important role in the onset and development of some leukemia. The advent of next-generation sequencing technologies using paired-end reads allows for the detection of gene fusions at unprecedented levels of efficiency and precision. However, the effective discovery of gene fusions in RNA-seq data requires the explicit consideration of several confounding factors such as non-specific alignments, intra-molecular rearrangements by cDNA library amplification, and mispriming of the sequence oligo-nucleotide within the bridge amplified cluster by Illumina. The scale of RNA-seq data is such that naive approaches often produce an unmanageable number of predictions, many of them likely to be false positive. Aims: We developed an algorithm, AccuFusion, that scales to whole transcriptome analysis of hundreds of patient samples for gene fusion detection and thus will prove useful for large datasets from The Cancer Genome Altlas (TCGA) and other large-scale cancer transcriptome sequencing projects. Methods: A given paired-end read alignment is quantified in terms of the genomic location (L) of the aligned read pair, the distance (D) between the aligned read pair of the fragment (insert) and the orientation (O) of the read pair, and the confidence that each read is uniquely aligned. The specific pattern in (L, D, O) space is used as a constraint to define the discordant read pair. For example, a discordant read pair may have an exceptionally long D spanning a region in the reference genome. This algorithm begins by detecting and clustering discordant read pairs that support the same fusion event (e.g. PML-RARA) and selects the discordant read clusters as fusion candidates. Next, a statistical model-based greedy algorithm is applied to define the boundaries of discordant read clusters to address the existence of multiple fusion products with different fusion junctions in the same fusion partners across samples. Meanwhile, an in-silico fragment length for each read pair within the discordant read clusters is calculated against the read cluster boundary, and any discordant read pair whose in-silico fragment length falls outside three standard deviations in the fragment size distribution is discarded as a false discordant read pair. An in-silico sequence generated by using the consensus of reads within discordant read clusters for each fusion candidate is used to detect breakpoint-spanning reads. In summary, the statistical model-based greedy algorithm and other filtering metrics are used to reduce the false positive rate of fusion candidates. Results: This software was applied to TCGA leukemia transcriptome sequencing projects and was used to analyze large number of samples simultaneously, it detected all known fusion products with different gene fusion junctions between PML and RARA in PML samples. It also detected some of those fusion events missed by other tools Summary/Conclusion: AccuFusion scales to whole transcriptome analysis of hundreds of patient samples for gene fusion detection and thus will prove useful for large datasets from The Cancer Genome Altlas (TCGA) and other large-scale cancer transcriptome sequencing projects. It is able to accurately detect fusion junctions/breakpoints for multiple fusion products with same fusion partner across samples. It is also able to detect the fusions with very weak signal by combining the signals across samples. Keywords: Acute myeloid leukemia